Joris Van den Bossche created ARROW-6305: --------------------------------------------
Summary: [Python] scalar pd.NaT incorrectly parsed in conversion from Python Key: ARROW-6305 URL: https://issues.apache.org/jira/browse/ARROW-6305 Project: Apache Arrow Issue Type: Bug Components: Python Reporter: Joris Van den Bossche When converting from scalar values, using {{pd.NaT}} (the missing value indicator that pandas uses for datetime64 data) results in an incorrect timestamp: {code} In [6]: pa.array([pd.Timestamp("2012-01-01"), pd.NaT]) Out[6]: <pyarrow.lib.TimestampArray object at 0x7f46c8368780> [ 2012-01-01 00:00:00.000000, 0001-01-01 00:00:00.000000 ] {code} where {{pd.NaT}} is converted to "0001-01-01", which is strange, as that does not even correspond with the integer value of pd.NaT. Numpy's version ({{np.datetime64('NaT')}}) is correctly handled. Which also means that a pandas Series holding pd.NaT is handled correctly (as when converting to numpy it is using numpy's NaT). Related to ARROW-842. -- This message was sent by Atlassian Jira (v8.3.2#803003)